https://github.com/crucials/twaddle

speech analysis app that collects statistics like words frequencies and transcribed text
https://github.com/crucials/twaddle

ai audio python python-eel speech-to-text vue whisper

Last synced: about 2 months ago
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speech analysis app that collects statistics like words frequencies and transcribed text

• Host: GitHub
• URL: https://github.com/crucials/twaddle
• Owner: crucials
• Created: 2024-04-04T07:58:48.000Z (6 months ago)
• Default Branch: master
• Last Pushed: 2024-07-22T03:17:52.000Z (2 months ago)
• Last Synced: 2024-07-22T10:58:04.940Z (about 2 months ago)
• Topics: ai, audio, python, python-eel, speech-to-text, vue, whisper
• Language: Vue
• Homepage:
• Size: 632 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# twaddle

transcribes speech and calculates stats like total spoken words count and 

all frequencies for all words

https://github.com/crucials/twaddle/assets/83793845/758fa9d5-6470-435d-8f72-9c675a8da9fd

## installation

this is a desktop app and it uses your pc resources to convert speech to text:

- transcription with cpu needs around 2.5gb of ram

- transcription with gpu needs around 1.5gb of video ram, cant be used yet

### linux

:penguin: [download on linux](https://github.com/crucials/twaddle/releases/download/v1.1.0-beta/twaddle-linux.zip)

### windows

requires [visual c++ redistributable 2015](https://learn.microsoft.com/en-us/cpp/windows/latest-supported-vc-redist) to be installed

🪟 [download on windows](https://github.com/crucials/twaddle/releases/download/v1.1.0-beta/twaddle-windows.zip)

### start manually

requires python and pip 

- download the project on the local machine

  ```bash

  git clone https://github.com/crucials/twaddle.git

  ```

- move to project's folder, install a shitpile of libraries

  ```bash

  pip install -r requirements.txt

  ```

- launch the ui

  ```bash

  python ./src/start_ui.py

  ```

## other info

built with python eel, vue and faster-whisper as a transcription model